Systems and methods for targeting consumers attitudinally aligned with determined attitudinal segment definitions

ABSTRACT

Disclosed herein are systems and methods for selecting a target group of consumers from a larger group of consumers in a computer database. Thus, for a given brand and marketing objective, the systems and methods provide for identifying the dimensions that define a relevant attitudinal consumer segment (or segments). In addition, the systems and methods select consumers, from an in-house or third party database containing appended variables, who are most attitudinally aligned with the target segment definition(s).

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.10/821,516, filed Apr. 9, 2004, now U.S. Pat. No. 7,472,072 which is acontinuation-in-part of U.S. patent application Ser. No. 09/511,971,filed Feb. 24, 2000, now abandoned. U.S. patent application Ser. No.10/821,516 also claimed the benefit of the earlier filing date of U.S.Provisional Patent Application No. 60/461,805, filed Apr. 11, 2003.

TECHNICAL FIELD

Disclosed embodiments herein relate generally to target marketingsystems and methods, and more particularly to customized targetmarketing systems and methods to identify consumer segments for a givenmarketing objective based on attitudinal dimensions, and to locateindividual consumers who are attitudinally aligned with the segmentdefinitions, for purposes of direct marketing.

BACKGROUND

In the quest for new business opportunities, there has been a growingproliferation of products and services seeking to more relevantlysatisfy consumer needs. This has heightened competition and furthered adesire by marketers to look for tools that can more precisely identifyoptimal groups of consumers. Previous targeting methods used historicalinformation to determine what type of consumer had previously usedproduct/service categories or brands. These factors were used to predictwhich consumers would likely buy in the future.

Previous approaches to target marketing prioritized consumers based oncategory and volume of brand usage. These consumer targeting effortswere largely based on demographic and geodemographic factors. A firstapproach typically involved the administration of a survey to measureconsumer usage levels pertaining to specific products, services andbrands. These surveys also gathered general demographic information foreach respondent. Standard analysis techniques were then applied to studythe results and identify optimal demographic segments for targetingmarketing efforts. Geodemographic systems were then developed thatcategorize the entire marketplace of consumers into a specific number ofneighborhood types. These neighborhood types were typically classifiedaccording to demographic factors.

Unfortunately, targeting methods based on demographics andgeodemographics have several drawbacks. For example, both methods assumethat all consumers within a defined demographic or geodemographicsub-set are equally attractive. As such, these methods typically do notdistinguish between individual consumers within the same group. Inaddition, neither method considers attitudinal variables, even thoughattitudinal variables greatly influence the future purchasing behaviorof consumers. Because of these drawbacks, volume-only marketingtechniques often do not meet the financial needs of marketers.Additionally, there has also been a growing consensus that demographicand other conventional targeting methodologies would be enhanced ifattitudinal filtering were also applied. In response to this, businessesare increasingly striving to find ways of identifying and reachinggroups of consumers who tend to “think alike” with respect to theirbrand and market segment. Some examples of groups divided based onattitudinal variables are:

-   -   Early adopters of high tech consumer products;    -   Risk-averse buyers of investment securities;    -   Prestige-seeking buyers of luxury automobiles;    -   Fashion conscious clothes buyers.

As may be seen in these examples, grouping of potential customers usingattitudinal characteristics and definitions results in segments definedby more than mere demographics and the like. For example, rather thancreating a group of potential luxury car buyers based on demographicinformation like income and past purchases, attitudinally-based segmentslook to the reasons for purchasing behavior. In this example, thisresults in a group of potential luxury car buyers that are grouped basedon the reason for purchasing a luxury car (e.g., seeking prestige,professional appearance, etc.).

There is therefore a high level of interest in a customized targetmarketing system based on attitudinal dimensions. While other methods ofattitudinal segmentation currently exist, there is need for a systemthat can also identify individual consumers who align attitudinally withthe segment definitions. This combination enables direct-to-consumercontact with attitudinally relevant products and marketing offers.Moreover, such a system would be even more beneficial to marketers byhaving the capability to help determine the attitudinally-based segmentdefinitions themselves, which may then be customized to each particularproduct being marketed.

BRIEF SUMMARY

Disclosed herein are systems and methods for selecting a target group ofconsumers from a larger group of consumers in a computer database. Thus,for a given brand and marketing objective, the systems and methodsprovide for identifying the dimensions that define a relevantattitudinal consumer segment (or segments). In addition, the systems andmethods select consumers, from an in-house or third party databasecontaining appended variables, who are most attitudinally aligned withthe target segment definition(s).

In one embodiment, a method includes providing at least non-attitudinalvariables for each consumer in the database, choosing a random subgroupof consumers from the larger group, and gathering attitudinal data,which is unavailable on the database, from each member of the subgroup.The method also includes creating attitudinal segments defined byattitudinal dimensions based on the attitudinal data, and assigning eachmember of the subgroup to one of the attitudinal segments using theattitudinal data corresponding to each member of the subgroup. Themethod includes identifying a plurality of the non-attitudinal variablesfor each member of the subgroup based on strength of relationshipbetween each of the non-attitudinal variables of the subgroup membersand the dimensions that define each member's corresponding attitudinalsegment. The method further includes calculating a probability score foreach member of the subgroup based on a degree of fit between each memberof the subgroup and their corresponding attitudinal segment. The methodalso provides for developing mathematical algorithms each correspondingto a separate one of the attitudinal segments and capable ofsubstantially predicting the probability score for each of the subgroupmembers with respect to their corresponding attitudinal segment usingthe identified plurality of non-attitudinal variables and theircorresponding calculated probability score. The method still furtherincludes calculating a probability score for each of the consumers inthe larger group based on a degree of fit between each of the consumersin the larger group and any of the attitudinal segments by applying atleast one of the developed algorithms to each consumer in the largergroup. Finally, the method provides for selecting the target group ofconsumers from the larger group based on the calculated probabilityscores of the consumers in the larger group.

In another aspect, a system includes a database storing the larger groupof consumers and storing at least non-attitudinal variables for eachconsumer in the database, a subgroup of consumers randomly selected fromthe larger group, and a list of attitudinal data unavailable on thedatabase gathered from each member of the subgroup, where theattitudinal data is based on attitudinal variables. In addition, thesystem further includes a computer coupled to the database, where thecomputer is configured to receive the list of attitudinal data.Furthermore, the computer is configured and programmed to createattitudinal segments defined by attitudinal dimensions based on thereceived attitudinal data, assign each member of the subgroup to one ofthe attitudinal segments using the attitudinal data corresponding toeach member of the subgroup, and to identify a plurality of thenon-attitudinal variables for each member of the subgroup based onstrength of relationship between each of the non-attitudinal variablesof the subgroup members and the dimensions that define each member'scorresponding attitudinal segment. The computer is also configured andprogrammed to calculate a probability score for each member of thesubgroup based on a degree of fit between each member of the subgroupand their corresponding attitudinal segment, and develop mathematicalalgorithms each corresponding to a separate one of the attitudinalsegments and capable of substantially predicting the probability scorefor each member of the subgroup with respect to their correspondingattitudinal segment using the identified plurality of non-attitudinalvariables and their corresponding calculated probability score. Thecomputer is still further configured and programmed to calculate aprobability score for each of the consumers in the larger group based ona degree of fit between each of the consumers in the larger group andany of the attitudinal segments by applying at least one of thedeveloped algorithms to each consumer in the larger group, and thenselect the target group of consumers from the larger group based on thecalculated probability score for each of the consumers of the largergroup.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, and the advantagesof the systems and methods herein, reference is now made to thefollowing descriptions taken in conjunction with the accompanyingdrawings, in which:

FIG. 1 illustrates one embodiment of a process of selecting consumersfor targeted marketing campaigns conducted according to the principlesdisclosed herein; and

FIG. 2 illustrates a computer-based system configured to selectconsumers for targeted marketing campaigns according to the principlesdisclosed herein.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Referring initially to FIG. 1, illustrated is one embodiment of aprocess of selecting consumers for targeted marketing campaignsconducted according to the principles disclosed herein. The processbegins at a start block 105. At block 110, a large database of potentialconsumers is provided. A consumer database contains a plurality of datavariables for each member of the group. Typically there are over 300variables, but this number can vary significantly. The variables canrelate to many different types of data. The data can fall intocategories including lifestyle, demographic, financial, home-ownership,vehicle registration, and consumer purchase behavior variables. A personskilled in the art will appreciate that one can include many differenttypes of consumer data variables on a consumer database. In a preferredembodiment, the database has lifestyle and demographic variables forover 85,000,000 individual consumers.

At block 115, a subgroup is selected from the larger database ofpotential customers. To carry out the marketing systems and methodsdisclosed herein, a user randomly selects a subgroup of consumers fromthe overall group contained on the database. In one embodiment, thesubgroup includes 20,000 people, however no limit is intended. Thesubgroup is randomly selected from the database using standard selectionsoftware that is well known in the art. Alternatively, it is possible topre-sort the group in order to select individuals based on pre-selectedvariables, which are typically objective variables. For example, one mayrandomly select a subgroup of individuals in the group of males between15-24 years of age. This may be appropriate for a particular type ofproduct such as disposable razors.

Turning to block 120, key client-specific attitudinal attributescorresponding to the particular product or service offered by the clientmay next be selected. Examples of such attitudinal attributes include,but are not limited to:

-   -   importance of quality over price    -   importance of price sensitivity in home computers    -   importance of brand name appeal to the consumer    -   preference for powerful cars over economy cars    -   brand name loyalty importance of value/price perceived        status/image of customer for using or wearing a brand name        product    -   importance of style/fashion    -   technology loving/hating    -   importance of convenience in selecting a retailer

Of course, other attributes that are based on the attitudes thatconsumers have when making the decision to purchase products or servicesare also envisioned as potential attitudinal attributes. Thus, theattitudinal attributes selected are not directed to purchase volumehistory, but rather towards the attitudes that consumers have, and whichrelated to future purchase decisions.

Once the attitudinal attributes have been selected, a survey (or othermeans for gathering attitudinal data) is conducted on the subgroupmembers, as shown in block 125, based on the attitudinal attributes.When creating the survey, attitudinal statements/questions are typicallycreated in such a way that elicits a quantitative response from thesubgroup members. Stated another way, the survey will frameattitude-based statements/questions in various ways to effectivelymeasure the degree of attitudinal commitment present in each surveyrespondent. One such technique involves exposing members of the subgroupto an attitudinal statement and asking them to rate their level ofagreement on a 5 point scale, where 1 represents “completely disagree”and 5 represents “completely agree”. Another approach involves givingmembers of the subgroup a set of attitude statements and asking them toidentify which statement is most important in their purchase decisionand which one is least. In one embodiment of the survey administered,approximately 1,500-2,000 completed surveys are typically employed forthe remainder of the process.

Next at block 130, the results of the survey are used to identify keyattitudinal dimensions. This process involves the use of “factoranalysis”. In this step, individual attitude statements displayingsurvey preferences that are correlated are grouped together to formattitudinal dimensions. For example, the statements: “I love to shop forstylish clothes”, “I would pay a premium for stylish clothes” and “Iprefer style over quality” could be grouped together to form theattitudinal dimension: “Stylish clothing is very important.” Factoranalysis looks for commonality. Commonality may be determined by lookingat exact matches of answers on the survey between several subgroupmembers. For example, scaled answers typically would include answerswhere members assign weights like “very important,” “of averageimportance” or “not very important” to specific attitudinal statements.In the end, factor analysis is used to group individual attitudinalstatements/questions together, thus creating multiple attitudinaldimensions at block 130, for use in identifying relevant consumersegments.

Next, at block 135, a number of attitudinal segments are created forsegregating the members of the subgroup, and each segment is defined bydefinitions based on one or more of the determined attitudinaldimensions found above. More specifically, based on the desired numberof segments, statistical techniques are applied to group individualswhose survey response patterns are characterized by at least twoelements of homogeneity. In a preferred embodiment, only two elements ofhomogeneity are employed, as judged against the total surveyedpopulation. First are the responses of the surveyed subgroup membersthat are overdeveloped in the same dimensions, and second are theresponses of the subgroup members that are underdeveloped incorresponding other dimensions. In addition, the disclosed process seeksto find groups of individuals whose response patterns are as mutuallyexclusive from members of other segments as possible. The result of thispart of the cluster analysis is the creation of the set of attitudinallydefined consumer segments. The following example illustrates a set ofattitudinal segmentation clusters for consumers who purchase children'sclothing:

-   -   a) “My children only need clothes that are functional and        durable.”    -   b) “I buy my children stylish clothes regardless of the price,”    -   c) “I buy my children the best clothes I can afford.”    -   d) “My children are deserving of the best clothes.”    -   e) “I dress my children so they'll be popular among their        friends.”    -   f) “I don't care about style; to me quality is the most        important factor.”

In this example, each of the above segments would have an underlying setof attitudinal dimensions.

The second part of this “cluster analysis” occurs at block 140, wheremembers of the subgroup are assigned to certain segments that have beenpreviously defined. The assignment of the subgroup members to specificsegments is done by determining which of the available segments'underlying attitudinal dimensions best fits with each member's patternof survey responses. A probability score is calculated at block 145 foreach individual in the subgroup based on the degree of fit of eachsubgroup member with the segment to which each member was assigned. Inone embodiment, these calculations occur at substantially the same timeas consumers are being segregated into their respective segment. This isaccomplished by comparing the responses of the individual members withthe attitudinal dimensions that provided the segment definition.Typically, the probability scores will be discriminating enough toillustrate distinctions between subgroup members who, for example,perfectly fit in a segment, very closely fit in a segment, do not fitvery close to a segment, and those that have opposite attitudes tomembers in a particular segment. As a result, although a number ofsubgroup members may be placed in the same segment, the probabilityscores provide for a ranking of the members in each segment based on howwell they fit in their segment.

At block 150, specific non-attitudinal variables common to each clusterof subgroup members are then identified. These will be used in thesubsequent development of mathematical algorithms, one related to eachof the segments created. Non-attitudinal variables (or sometimes called“non-behavioral variables” as in U.S. application Ser. No. 09/511,971cited above) are objective variables of each consumer that are not basedon the purchasing attitudes of the consumer. Examples of non-attitudinalvariables include, but are not limited to, gender, income, age,home-ownership, parenthood, education, geographic location, ethnicity,etc. Such variables do not include attitudinal variables like brandloyalty, price sensitivity, importance of quality, preference for style,and attraction to brand proposition. These segment-specificnon-attitudinal variables would be a subset of all the non-attitudinalvariables stored in the database. In an advantageous embodiment, thedatabase includes over 300 non-attitudinal variables for each consumer,however any number of non-attitudinal variables may be available.

In an advantageous embodiment, such segment-specific non-attitudinalvariables are identified as those that best correlate with the subgroupmembers who have been assigned to a particular segment. In someembodiments, at least two of the identified non-attitudinal variablesmay be cross-correlated, that is, they share a common causalrelationship and therefore are not independent of each other. In suchcases one of these variables will be eliminated, since using both mayoverstate the degree of importance given to the common characteristic.

At block 155 a predictive mathematical algorithm is developed for eachsegment. Each algorithm uses the segment-specific non-attitudinalvariables to predict the probability scores given to each of thesubgroup members belonging to that particular segment. Each algorithmtypically uses linear regression. Specifically, the probability scoresof the subgroup members (i.e., based on degree of fit of those membersrelative to the segment definition) are employed as the dependentvariables in the development of the predictive algorithms. Oncedeveloped, the algorithms will compute a probability score for anyindividual in a database who has the segment-specific attributesappended. At block 160, the algorithm for a particular segment isapplied to all consumers in the database to calculate a probabilityscore for each member in the database that represents that person'sdegree of fit with that segment definition. The higher the score, thebetter the fit within that segment. In addition, multiple algorithms maybe applied to the consumers in the database to determine which segmentthey most closely fit. In either embodiment, all of the consumers in thedatabase may be ranked in order based on their resulting probabilityscores, and this order would typically change for each of the differentsegments/algorithms employed.

In an exemplary embodiment, the optimal target group of consumersselected from the database represents about 5% to 25% of the top-rankedconsumers in the database, however any size target group may be selecteddepending on marketing requirements and the level of predictive accuracythat is acceptable to the client. In this manner, the system createsattitudinal segments that mirror the segments previously created for themembers of the subgroup. Furthermore, it assigns optimally-aligned(i.e., the same or substantially similar attitudinal attributes) targetconsumers from the larger database accordingly. As result of themethodology described above, the non-attitudinal variables (asmathematically weighted by the algorithm) become predictive ofattitudinal variables not available on the database. If a segmentationcluster is defined by attitudinal attributes that are desirable to themarketer, these identified non-attitudinal variables can now be used topredictively identify members in that cluster. The process then ends atblock 165 where the selected consumers are targeted for the marketing ofgoods and/or services.

What follows is an example of the development of a mathematicalalgorithm for attitudinal segment “X”, which further illustrates anapplication of the disclosed process. In accordance with theabove-mentioned process, nine exemplary non-attitudinal variables thatcould apply to attitudinal segment “X” are shown in Table 1. These aretypically found on the database associated with each of the consumers inthe database.

TABLE 1 Name of Non-Attitudinal Value for Variable VariableConfiguration an Individual 1) Value of home Expressed as an index: 147($ value of individual's home/average value of neighborhood homes × 100)2) Time in current Years 5 residence 3) Purchase beauty “yes” = 1; “no”= 0 0 aids 4) Subscribe to a “yes” = 1; “no” = 0 1 fitness magazine 5)Read the Bible “yes” = 1; “no” = 0 0 6) Surf the internet “yes” = 1;“no” = 0 1 7) Purchase by mail “yes” = 1; “no” = 0 0 order 8) Donate to“yes” = 1; “no” = 0 0 environmental causes 9) Age 18-24 “yes” = 1; “no”= 0 1

The values in the column labeled “Value for an Individual” will be usedto illustrate the development and application of the mathematicalalgorithms corresponding to attitudinal segment “X”. Accordingly, atypical mathematical algorithm correlating to attitudinal segment “X”may thus be developed as disclosed herein using the non-attitudinalvariables as the independent variables and the probability score for asubgroup member (the probability that that individual is a member ofattitudinal segment “X”) as the dependant variable. The algorithm is setforth in equation (1):

$\begin{matrix}{{Probability} = \frac{\begin{matrix}{33.47 + {0.68\begin{pmatrix}\begin{matrix}{Value} \\{of}\end{matrix} \\{Home}\end{pmatrix}} - {0.94\begin{pmatrix}{Time\_ in} \\{Current} \\{Residence}\end{pmatrix}} - {13.5\begin{pmatrix}{Purchases} \\{Beauty} \\{Aids}\end{pmatrix}} +} \\{{17.71\begin{pmatrix}{Subscribes} \\{to\_ Fitness} \\{Magazines}\end{pmatrix}} - {14.36\begin{pmatrix}{Reads} \\{the} \\{Bible}\end{pmatrix}} + {10.00\begin{pmatrix}{Surfs} \\{the} \\{Internet}\end{pmatrix}} -} \\{{20.94\begin{pmatrix}{Purchases} \\{By\_ Mail} \\{Order}\end{pmatrix}} + {9.07\begin{pmatrix}{Donates\_ to} \\{Environmental} \\{Causes}\end{pmatrix}} + {11.67\begin{pmatrix}{Age} \\{18 - 24}\end{pmatrix}}}\end{matrix}}{100}} & (1)\end{matrix}$

Once the algorithm has been developed and is ready to be applied to adatabase, a computer (see FIG. 2) is configured and programmed to insertthe values of the non-attitudinal variables for each individual into theformula, and calculates the corresponding probability score. Using thevalues from TABLE 1, equation (2) sets forth the algorithm:

$\begin{matrix}{{{Probability} = {\frac{\begin{matrix}{33.47 + {0.68(147)} - {0.94(5)} - {13.5(0)} + {17.71(1)} -} \\{{14.36(0)} + {10.00(1)} - {20.94(0)} + {9.07(0)} + {11.67(1)}}\end{matrix}}{100} = {78.146\%}}}\mspace{340mu}} & (2)\end{matrix}$

In the end, the attitudinal approach disclosed herein may bedistinguished from other consumer targeting systems because of itsidentification of non-attitudinal variables based on attitudinalattributes for consumers, rather than on consumers' purchase volumehistory. Thus, the disclosed approach can identify-consumers fortargeted marketing that may have never purchased a vendor's product inthe past, but have the attitudinal attributes that are the same orsimilar to the type of person who has and does purchase the vendor'sproduct. For example, if the vendor sells luxury cars, a conventionalmarketing analysis will typically identify consumers who have a highincome. However, it is clear that not all high-income earners choose toown the same brand of luxury car. The disclosed approach will assist thevendor by identifying a subset of high-income earners with attitudinalcharacteristics similar that predispose them to their particular brand'sfeatures and image. As a result, the vendor may now target potentialpurchasers who would likely not have been identified by systemsemploying purchase history criteria, demographics, or lifestylescriteria. This approach will improve efficiency and reduce marketingcosts.

Thus, by identifying potential consumers for targeting using thedisclosed process, as well as using systems employing such a process,several advantages are realized. For instance, the disclosed approachprovides the advantage of requiring the attitudinal survey to beadministered to only a fraction of the entire database population,typically 1,500-2,000 consumers. Customer databases generally containmillions of names and a comprehensive survey would be prohibitivelyexpensive. In addition, it is not necessary to know in advance theattitudinal segment definitions, since the system is fully capable ofderiving these. More specifically, in place of block 120 describedabove, the disclosed approach may be used to discover desirableattitudinal attributes (rather than knowing them ahead of time) byconducting a survey as described above, and then clustering the subgroupmembers into segments based on the similarity and differences of theirresponses. Once the members are clustered into segments based on surveyresponse, the prevailing attitudinal attributes for each segment may beobserved.

Turning now to FIG. 2, illustrated is a computer-based system 200configured to select consumers for targeted marketing campaignsaccording to the principles disclosed herein. The system 200 includes alarge database 205 of consumers, which may be similar to the databasediscussed with reference to FIG. 1. As illustrated, the database 205 ofconsumers is configured to contain substantial information about theconsumers, including non-attitudinal variables 210 particular to eachstored consumer. These non-attitudinal variables 210 may be in additionto any number of variables, including purchase transaction variables, asdiscussed above.

To identify potential consumers based on attitudinal characteristics andattributes, the system 200 is configured to select a subgroup 215 fromthe larger database 205 of potential customers. As before, there is nolimit to the size of the selected subgroup 215. In a specificembodiment, the subgroup 215 is randomly selected from the database 205using conventional selection software running on a computer. Suchsoftware may also pre-sort the subgroup 215 based on pre-selectedcharacteristics, as discussed above. The system 200 also includes asurvey 220 that is created to include attitudinal statements thatelicits quantitative responses from the subgroup members, and thus willframe attitude-based statements/questions in various ways to effectivelymeasure the degree of attitudinal commitment present in each subgroupmember. These attitudinal statements are based on specific attitudinalattributes, such as those listed above, that correspond to theparticular product or service advertised by the vendor. The survey isthen conducted on the subgroup members 215, as illustrated.

The system 200 still further includes one or more computers or othertypes of computing device 225 coupled to the database 205. The computer225 may be of conventional design, but be configured to receive thesurvey 220 questions and results for processing in accordance with theprinciples disclosed herein. Specifically, the computer 225 isconfigured to conduct the “factor analysis,” e.g., the responses tosurvey 220 are statistically organized into attitudinal dimensions. Thecomputer 225 then applies statistical techniques to create a list ofattitudinally-defined segments 230, and then segregates (“clusters”) thesubgroup members 215 into those segments based on the homogeneity ofsurvey 220 response patterns among members (relative to the dimensions).The computer 225 is also configured to compute and append a probabilityscore for each individual in the segmented clusters 230. As before, thiscalculation is based on the degree of fit of each subgroup member withthe segment to which each member was assigned. This is done by comparingthe responses of the members to the attitudinal dimensions the computer225 used to actually define the segments.

For each subgroup segment in the list 230, the computer 225 alsodetermines the subset of non-attitudinal variables 235 (from thoseavailable in 210) which best correlate with the members it has assignedto the respective segment. These non-attitudinal variables may beidentified using the technique disclosed above. The computer 225 is alsoconfigured to develop a predictive mathematical algorithm 237 for eachsegment. Each algorithm uses the segment-specific non-attitudinalvariables 235 to predict the probability scores given to each of thesubgroup members belonging to the particular segment. The algorithmstypically use step-wise linear regression. Specifically, the probabilityscores (i.e., degree of fit of members relative to the segmentdefinition) are employed as the dependent variables in the developmentof the predictive algorithms.

Once developed, the algorithms 237 will be able to compute a probabilityscore for all individuals in database 205 with the segment-specific,non-attitudinal variables 235 appended. Once applied to the database205, each individual will receive an appended probability scoreindicating their degree of fit with each segment. For example, if thecomputer 225 has been configured to identify five segments in 230, therewill be five probability scores appended to each name in 205, one persegment. For any given segment, the names in database 205 will then berank ordered by the computer 225 based on the corresponding probabilityscores; the higher the score the better the fit in the particularsegment. The resulting list of target consumer names 240 may now be usedby a vendor to target potential customers. Since those at the top of thelist will typically have a better fit with the segment definition thanthose further down, vendors will typically select names in the top 5-25%of the list for marketing purposes (although further penetration of thelist is possible). The computer 225 will be configured to provide thedesired selection of names from 240. As a result, system 200 will haveidentified potential purchasers who would likely not have beenidentified by systems employing purchase history criteria, demographics,or lifestyle criteria. This would improve efficiency and reducemarketing costs.

Also, in accordance with the process discussed above, the computer 225may be configured to actually discover the underlying attitudinaldimensions that form the basis for segment definitions, rather thanbeing programmed with them ahead of time. This is a valuable product ofthe disclosed systems and methods because frequently clients are unawareof how a database or market can be segmented along attitudinal lines.This is accomplished by the computer 225 processing the survey resultsin the fashion described above, and then creating attitudinal dimensionsand defining attitudinal segments based on the similarity anddifferences of survey responses and response patterns. Once thesesegments are so defined, the prevailing attitudinal attributes for eachsegment may be observed. Of course, any of the processing capabilitiesof the computer 225, as well as other potential components in the system200, may be embodied in either hardware or software, or both, withoutlimitation.

While various embodiments of systems and methods for selecting consumersfor targeted marketing campaigns based on attitudinal attributes of theconsumers have been described above, it should be understood that theyhave been presented by way of example only, and not limitation. Thus,the breadth and scope of the invention(s) should not be limited by anyof the above-described exemplary embodiments, but should be defined onlyin accordance with any claims and their equivalents issuing from thisdisclosure. Furthermore, the above advantages and features are providedin described embodiments, but shall not limit the application of suchissued claims to processes and structures accomplishing any or all ofthe above advantages.

Additionally, the section headings herein are provided for consistencywith the suggestions under 37 CFR 1.77 or otherwise to provideorganizational cues. These headings shall not limit or characterize theinvention(s) set out in any claims that may issue from this disclosure.Specifically and by way of example, although the headings refer to a“Technical Field,” such claims should not be limited by the languagechosen under this heading to describe the so-called technical field.Further, a description of a technology in the “Background” is not to beconstrued as an admission that technology is prior art to anyinvention(s) in this disclosure. Neither is the “Brief Summary” to beconsidered as a characterization of the invention(s) set forth in issuedclaims. Furthermore, any reference in this disclosure to “invention” inthe singular should not be used to argue that there is only a singlepoint of novelty in this disclosure. Multiple inventions may be setforth according to the limitations of the multiple claims issuing fromthis disclosure, and such claims accordingly define the invention(s),and their equivalents, that are protected thereby. In all instances, thescope of such claims shall be considered on their own merits in light ofthis disclosure, but should not be constrained by the headings set forthherein.

1. A method, implemented at least in part by a computing device, themethod comprising: with the computing device, receiving attitudinal dataassociated with a first plurality of consumers; with the computingdevice, associating a first subgroup of the first plurality of consumerswith a first attitudinal segment based on the received attitudinal data;with the computing device, determining a first attitudinal segment basedscore for at least one consumer in the first plurality of consumers,wherein determining the first attitudinal segment based score comprisesdetermining the score based on: attitudinal data associated with the atleast one consumer in the first plurality of consumers; and at least oneattitudinal dimension associated with the first attitudinal segment; andwith the computing device, associating at least one non-attitudinalvariable with each consumer in the first plurality of consumers, whereina value of the at least one non-attitudinal variable associated with afirst one of the first plurality of consumers is different from a valueof the at least one non-attitudinal variable associated with a secondone of the first plurality of consumers.
 2. The method of claim 1,wherein associating the subgroup comprises associating the subgroupbased on at least one attitudinal dimension associated with the firstattitudinal segment.
 3. The method of claim 1, further comprisingassociating a second subgroup of the first plurality of consumers with asecond attitudinal segment based on the received attitudinal data. 4.The method of claim 3, wherein associating the consumers associated withthe second subgroup comprises associating consumers who are exclusive ofthe consumers associated with the first subgroup.
 5. The method of claim1, further comprising determining a second attitudinal segment basedscore for the at least one consumer.
 6. The method of claim 1, furthercomprising determining at least one attitudinal segment based score foreach consumer in the first plurality of consumers.
 7. The method ofclaim 1, wherein associating the at least one non-attitudinal variablecomprises associating an objective variable.
 8. The method of claim 1,further comprising defining at least one attitudinal dimension based onthe received attitudinal data.
 9. The method of claim 1, furthercomprising defining the first attitudinal segment based on the receivedattitudinal data.
 10. The method of claim 1, further comprising definingat least one additional attitudinal segment based on the receivedattitudinal data.
 11. The method of claim 1, further comprising:defining a plurality of attitudinal dimensions based on the receivedattitudinal data; and defining a plurality of additional attitudinalsegments based on the defined attitudinal dimensions.
 12. The method ofclaim 1, further comprising defining a predetermined number ofadditional attitudinal segments.
 13. The method of claim 1, whereinassociating the at least one non-attitudinal variable with each consumerin the first plurality of consumers comprises associating a plurality ofnon-attitudinal variables with each consumer in the first plurality ofconsumers.
 14. The method of claim 1, further comprising determining arelationship between the following: the first attitudinal segment basedscores for each consumer in the first plurality of consumers; and thenon-attitudinal variables associated with the consumers in the firstplurality of consumers.
 15. The method of claim 14, wherein determiningthe relationship comprises performing a regression analysis.
 16. Themethod of claim 1, further comprising utilizing a predictive model togenerate a first attitudinal segment based score per consumer for eachconsumer in a second plurality of consumers.
 17. The method of claim 16,further comprising determining a ranking of the first attitudinalsegment based scores for the second plurality of consumers.
 18. Themethod of claim 16, further comprising utilizing the predictive model togenerate a second attitudinal segment based score per consumer for eachconsumer in the second plurality of consumers.
 19. The method of claim18, further comprising determining a ranking of the second attitudinalsegment based scores for the second plurality of consumers.